In the medical field, simulators such as operation simulators, organ simulators and the like are used to determine treatment plans, perform diagnosis, predict postoperative conditions, develop medical supplies and equipment and the like. In the simulation using these kinds of simulators, 3-dimensional shape data of an organ is used, however, often the generation of the 3-dimensional shape data of the organ is not easy. This is because the organs are located inside the body, so visual observation and direct measurement of the organs are not possible, and the shapes of the organs are very complex, fundamentally.
The following two methods, for example, are known as methods for generating 3-dimensional shape data of the organs. First, (1) a first method is a method in which a doctor observes tomographic images such as Computer Tomography (CT) images, Magnetic Resonance Imaging (MRI) images or the like, sets the boundaries of each portion of the organ, and draws boundary lines. Also, (2) a second method is a method in which 3-dimensional shape data of a reference organ is prepared in advance, and by transforming that shape, the shape of the organ is obtained for each individual patient.
However, in the former method, there is a problem in which it is difficult to set boundaries, when the tomographic image is unclear due to unevenness in the contrast medium, operation scars and the like. Moreover, a doctor having knowledge and experience must draw boundary lines on hundreds of tomographic images, so the workload is large.
In regards to the latter method, transformation is carried out by correlating points in the reference shape with points in the target shape, however, there is a problem in which, if the points that are to be correlated are not set properly, the transformation cannot be carried out well.
As for the latter method, there exists a conventional technique such as described below. More specifically, a predicted shape model is expressed using triangular patches and the vertices of those patches; and for each triangular patch, observation data is searched for in the normal direction from the center of gravity of the triangular patch. When the observation data is found from this search, the predicted shape model is transformed by adding a force such that the center of gravity of the patch moves toward the observation data. However, in this technique, there was a problem in which, when normal lines cross, an unnatural shape will occur.